Counterfactual Reasoning in Observational Studies
- Negar Hassanpour, Department of Computing Science, University of Alberta
To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures)
Citation
N. Hassanpour. "Counterfactual Reasoning in Observational Studies". National Conference on Artificial Intelligence (AAAI), February 2019.Keywords: | counterfactual reasoning, machine learning |
Category: | In Conference |
Web Links: | Conference Link |
BibTeX
@incollection{Hassanpour:AAAI19, author = {Negar Hassanpour}, title = {Counterfactual Reasoning in Observational Studies}, booktitle = {National Conference on Artificial Intelligence (AAAI)}, year = 2019, }Last Updated: February 19, 2020
Submitted by Russ Greiner